The performance of a parallel computer system remarkably varies for each application. Accordingly, its performance evaluation is important. A performance evaluation method of the parallel computer system includes (1) a method in which a specific processing is executed by various computers and a comparison is made, and (2) a self-complete type method to evaluate how much performance a certain computer demonstrates as compared with its own potential. The former is mainly used for performance comparison among computers as a benchmark test. The latter is required to be executed in practical use after introduction. Although the self-complete type performance evaluation can be carried out by using an index called a parallel efficiency, it has not been actually executed. Besides, although a parallel performance evaluation (so-called scalability evaluation) can also be made instead of the calculation of the parallel efficiency, in which time measurements are carried out while the number p of processors is changed, and a comparison of the decrease degree with an ideal decrease degree of 1/p is made, since it is necessary to make measurements several times, the evaluation is not made in general. Besides, the scalability evaluation is qualitative, and a strict parallel performance evaluation cannot be performed. Accordingly, at present, processings with poor parallel efficiency cannot be detected, and they are put in an ungoverned state.
The performance evaluation of a parallel processing by using the parallel efficiency is performed by calculating a parallel efficiency Ep(p) determined by expressions (1) and (2) set forth below. Where, p is the number of processors, τ(1) is a processing time in a case where a processing is executed by one processor, τ(p) is a processing time in a case where the same processing is executed by p processors, and τi(p) is a processing time of an i-th processor under 1≦i≦p.
                                          E            p                    ⁡                      (            p            )                          ≡                              τ            ⁡                          (              1              )                                                          τ              ⁡                              (                p                )                                      ·            p                                              (        1        )                                          τ          ⁡                      (            p            )                          ≡                              Max                          i              =              1                        p                    ⁡                      (                                          τ                i                            ⁡                              (                p                )                                      )                                              (        2        )            
The expression (1) is disclosed in, for example, a document “PERFORMANCE EVALUATION OF GeoFEM OF PARALLEL FINITE ELEMENT METHOD CODE, Transactions of JSCES, No. 20000022 (2000) by Tsukaya, Nakamura, Okuda, and Yagawa”.
However, even if the parallel efficiency is determined by the conventional method, since the quantitative relation to parallel performance impediment factors is not clear, it has not been understood which impediment factor has what influence on the parallel efficiency. Besides, in a certain parallel performance evaluation technique (For example, Japanese Patent Application No. 2001-241121, and US Publication No. US-2003-0036884-A1), as shown in FIG. 1, there is required a condition “load balance is kept, and respective processing times γi (parallel part), χi,1 (redundancy processing part), χi,2 (communication part), or χi,others (other parallel performance impediment factors) are identical to one another for all “i””, and there has been a problem that it can be applied to only a certain special parallel processing.
Besides, it is difficult to apply the conventional methods to the parallel processing by a grid or a cluster. This is because when resources distributed on the grid or cluster and required for calculation, such as memories, data and CPUs, are concentrated in one processor, there often occurs a case where the processing becomes so large that it can not be accomplished by the one processor. That is, it is difficult to measure τ(1) itself. Besides, to obtain τ(1) and τ(p) in the expression (1) by actual measurement supposes that the performances of processors are identical to one another. However, since the respective processor performances on the grid or the cluster are generally different from one another, there is also a problem that even if the actually measured τ(1) and τ(p) are substituted into the expression (1), an accurate parallel efficiency cannot be determined.